Explainability Solutions
🔍 SHAP Analysis
SHapley Additive exPlanations for feature importance.
- Feature contributions
- Global explanations
- Local explanations
- Dependency plots
🎯 LIME
Local Interpretable Model-agnostic Explanations.
- Instance explanations
- Surrogate models
- Text explanations
- Image explanations
📊 Feature Importance
Understand which features drive predictions.
- Permutation importance
- Drop-column importance
- Model-specific methods
- Feature ranking
🧠 Attention Viz
Visualize attention in deep learning models.
- Attention weights
- Saliency maps
- Grad-CAM
- Transformer attention
📋 Model Cards
Document model behavior and limitations.
- Performance metrics
- Intended use
- Ethical considerations
- Limitations
⚖️ Fairness Analysis
Detect and mitigate bias in models.
- Bias metrics
- Group fairness
- Individual fairness
- Mitigation strategies
XAI Techniques
SHAP
Game theory approach
LIME
Local surrogates
Grad-CAM
Visual explanations
Decision Trees
Interpretable models
Partial Dependence
Feature effects
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